docs: def summary(self, stdout=True, plot=False):
          '''
          Displays diagnostics to the user
  
          Args:
              stdout (bool): print results to the console
              plot (bool): use Seaborn to plot results
          '''
          if stdout:
              print('Collinearity summary:')
              print(pd.concat([self.results['Eigenvalues'],
                               self.results['ConditionIndices'],
                               self.results['VIFs'],
                               self.results['CorrelationMatrix']],
                              axis=1))
  
              print('Outlier summary:')
              print(self.results['RowMahalanobisDistances'])
              print(self.results['ColumnMahalanobisDistances'])
  
              print('Validity summary:')
              print(self.results['Variances'])
  
          if plot:
              verify_dependencies('seaborn')
              for key, result in self.results.items():
                  if key == 'CorrelationMatrix':
                      ax = plt.axes()
                      sns.heatmap(result, cmap='Blues', ax=ax)
                      ax.set_title(key)
                      sns.plt.show()
                  else:
                      result.plot(kind='bar', title=key)
                      plt.show()